According to the theory of robust learning [1]}, [2]}, noisy examples tend to have relatively high loss values. However, the hard yet clean examples which fall near the classification boundary also have high losses [3]}. These hard clean examples play an important role to boost the recommendation performance of implicit feedback [4]}, [5]}, [6]}. Denoising methods based on loss values could encounter difficulties to distinguish between noisy examples and hard clean examples, leading to sub-optimal solutions. However, we find that another signal which can help to identify noisy examples can be obtained from the comparison between different model predictions.
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